{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e39fa3e1",
   "metadata": {},
   "source": [
    "# Demo for ROSHAMBO (version 0.0.1)\n",
    "\n",
    "In this notebook, we will provide instructions on how to install ROSHAMBO. We will then demonstrate how to use ROSHAMBO with various examples, highlighting the flexibility offered by the available parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1a20e12",
   "metadata": {},
   "source": [
    "## Installation Instructions for ROSHAMBO\n",
    "This section will guide you through the steps to install the ROSHAMBO package for our demonstration. Please follow the steps in the provided order.\n",
    "\n",
    "### Step 1: Create a New Conda Environment\n",
    "First, we are going to set up a new conda environment specifically for this demo. This will prevent any conflicts with your existing Python installations.\n",
    "\n",
    "Run the following commands in your terminal:\n",
    "\n",
    "`conda create --name roshambo-demo python=3.9.6`\n",
    "\n",
    "`conda activate roshambo-demo`\n",
    "\n",
    "<span style=\"color:red;\">Note that you need to use a Python version from the same minor-level release as that used when building Boost, which is required for RDKit.</span>\n",
    "\n",
    "\n",
    "### Step 2: Install Required Packages\n",
    "Next, we need to install the notebook package in our new environment so that we can run the Jupyter notebook.\n",
    "\n",
    "In the same terminal and with the roshambo-demo environment activated, run:\n",
    "\n",
    "`conda install notebook`\n",
    "\n",
    "### Step 3: Load/Install Necessary Modules/Dependencies\n",
    "Some parts of ROSHAMBO rely on RDKit and CUDA. You need to compile RDKit (https://www.rdkit.org/docs/Install.html#installing-prerequisites-from-source).\n",
    "Note that you should enable support for generating InChI strings and InChI keys by adding the argument -DRDK_BUILD_INCHI_SUPPORT=ON to your cmake command line.\n",
    "\n",
    "After successfully compiling RDKit, define the following environment variables:\n",
    "\n",
    "`export RDBASE=/path/to/your/rdkit/installation`\n",
    "\n",
    "`export RDKIT_LIB_DIR=$RDBASE/lib`\n",
    "\n",
    "`export RDKIT_INCLUDE_DIR=$RDBASE/Code`\n",
    "\n",
    "`export RDKIT_DATA_DIR=$RDBASE/Data`\n",
    "\n",
    "`export PYTHONPATH=$PYTHONPATH:$RDBASE`\n",
    "\n",
    "You also need to make sure that CUDA is available.\n",
    "\n",
    "### Step 4: Install ROSHAMBO\n",
    "Finally, let's install ROSHAMBO. You can do this by first cloning the Github repository.\n",
    "\n",
    "To clone from Github, use:\n",
    "\n",
    "`git clone https://github.com/rashatwi/roshambo.git`\n",
    "\n",
    "After cloning the repository, navigate to the directory and install it using pip3:\n",
    "\n",
    "`cd roshambo`\n",
    "\n",
    "`pip3 install .`\n",
    "\n",
    "Now, you should have the ROSHAMBO package installed and ready to go for our demonstration!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e8568de",
   "metadata": {},
   "source": [
    "## General imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2deada66",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "import shutil\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ca1885",
   "metadata": {},
   "source": [
    "## Basic ROSHAMBO Run\n",
    "\n",
    "In the following code cell, we start with a basic run of the `get_similarity_scores` function from the ROSHAMBO API. Our input molecules, provided in the .sdf format, are processed without generating any conformers. We utilize the default \"color\" force field in this operation. We use the \"analytic\" method, second order, for calculating the shape overlap volume with the default cutoff radius (epsilon = 0.1). \n",
    "\n",
    "Note that the `ref_file` and `dataset_files_pattern` should be in the same `working_dir` specified as input to the function and that `dataset_files_pattern` can represent a pattern (marked with an *) that matches multiple files, not just a single file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b82c457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.4731025695800781\n",
      "Preparing mols took: 0.7892048358917236\n",
      "Run time: 0.2777021930087358\n",
      "Running paper took: 0.31625962257385254\n",
      "Converting transformation arrays took: 0.0033507347106933594\n",
      "Transforming molecules took: 0.06539654731750488\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating shape scores took: 0.622523307800293\n",
      "Calculating color scores took: 0.46125268936157227\n",
      "Creating dataframe took: 0.1764819622039795\n",
      "Writing molecule file took: 0.24007058143615723\n"
     ]
    }
   ],
   "source": [
    "from roshambo.api import get_similarity_scores\n",
    "\n",
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.sdf\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=0,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    working_dir=\"data/basic_run\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b5c2bdb",
   "metadata": {},
   "source": [
    "After running this cell, you will find three new files in the data/basic_run directory:\n",
    "\n",
    "1. **mols.sdf**: This file contains the preprocessed molecules that are fed to the PAPER method internally.\n",
    "\n",
    "2. **hits.sdf**: This file includes the transformed structures with hydrogen atoms added, ordered accordingly to the score specified by `sort_by`.\n",
    "\n",
    "3. **roshambo.csv**: This file contains the hit scores ranked by the score specified by `sort_by`, \"ComboTanimoto\" in this case. \n",
    "\n",
    "Let's look at the content of the roshambo.csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c33e0dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule</th>\n",
       "      <th>OriginalName</th>\n",
       "      <th>ComboTanimoto</th>\n",
       "      <th>ShapeTanimoto</th>\n",
       "      <th>ColorTanimoto</th>\n",
       "      <th>FitTverskyCombo</th>\n",
       "      <th>FitTversky</th>\n",
       "      <th>FitColorTversky</th>\n",
       "      <th>RefTverskyCombo</th>\n",
       "      <th>RefTversky</th>\n",
       "      <th>RefColorTversky</th>\n",
       "      <th>Overlap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL221029_0</td>\n",
       "      <td>CHEMBL221029</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1166.528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL220585_0</td>\n",
       "      <td>CHEMBL220585</td>\n",
       "      <td>1.762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>1.919</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.919</td>\n",
       "      <td>1.817</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.817</td>\n",
       "      <td>1166.285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL557844_0</td>\n",
       "      <td>CHEMBL557844</td>\n",
       "      <td>1.416</td>\n",
       "      <td>0.758</td>\n",
       "      <td>0.659</td>\n",
       "      <td>1.609</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.774</td>\n",
       "      <td>1.707</td>\n",
       "      <td>0.891</td>\n",
       "      <td>0.816</td>\n",
       "      <td>1043.744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL221912_0</td>\n",
       "      <td>CHEMBL221912</td>\n",
       "      <td>1.386</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.521</td>\n",
       "      <td>1.730</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.615</td>\n",
       "      <td>1046.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL221037_0</td>\n",
       "      <td>CHEMBL221037</td>\n",
       "      <td>1.223</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.553</td>\n",
       "      <td>1.410</td>\n",
       "      <td>0.733</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.638</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1047.908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHEMBL222027_0</td>\n",
       "      <td>CHEMBL222027</td>\n",
       "      <td>1.180</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.487</td>\n",
       "      <td>1.366</td>\n",
       "      <td>0.753</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.702</td>\n",
       "      <td>1058.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEMBL221376_0</td>\n",
       "      <td>CHEMBL221376</td>\n",
       "      <td>1.170</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.533</td>\n",
       "      <td>1.345</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.638</td>\n",
       "      <td>1.630</td>\n",
       "      <td>0.866</td>\n",
       "      <td>0.764</td>\n",
       "      <td>1023.373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL375296_0</td>\n",
       "      <td>CHEMBL375296</td>\n",
       "      <td>1.154</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.534</td>\n",
       "      <td>1.352</td>\n",
       "      <td>0.689</td>\n",
       "      <td>0.663</td>\n",
       "      <td>1.593</td>\n",
       "      <td>0.859</td>\n",
       "      <td>0.734</td>\n",
       "      <td>1016.538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHEMBL1081138_0</td>\n",
       "      <td>CHEMBL1081138</td>\n",
       "      <td>1.135</td>\n",
       "      <td>0.730</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.403</td>\n",
       "      <td>0.791</td>\n",
       "      <td>0.612</td>\n",
       "      <td>1.450</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.546</td>\n",
       "      <td>1062.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHEMBL1079594_0</td>\n",
       "      <td>CHEMBL1079594</td>\n",
       "      <td>1.133</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.419</td>\n",
       "      <td>1.254</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.523</td>\n",
       "      <td>1.647</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1152.017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Molecule   OriginalName  ComboTanimoto  ShapeTanimoto  \\\n",
       "0   CHEMBL221029_0   CHEMBL221029          2.000          1.000   \n",
       "1   CHEMBL220585_0   CHEMBL220585          1.762          1.000   \n",
       "2   CHEMBL557844_0   CHEMBL557844          1.416          0.758   \n",
       "3   CHEMBL221912_0   CHEMBL221912          1.386          0.866   \n",
       "4   CHEMBL221037_0   CHEMBL221037          1.223          0.671   \n",
       "5   CHEMBL222027_0   CHEMBL222027          1.180          0.694   \n",
       "6   CHEMBL221376_0   CHEMBL221376          1.170          0.637   \n",
       "7   CHEMBL375296_0   CHEMBL375296          1.154          0.619   \n",
       "8  CHEMBL1081138_0  CHEMBL1081138          1.135          0.730   \n",
       "9  CHEMBL1079594_0  CHEMBL1079594          1.133          0.714   \n",
       "\n",
       "   ColorTanimoto  FitTverskyCombo  FitTversky  FitColorTversky  \\\n",
       "0          1.000            2.000       1.000            1.000   \n",
       "1          0.762            1.919       1.000            0.919   \n",
       "2          0.659            1.609       0.835            0.774   \n",
       "3          0.521            1.730       0.958            0.773   \n",
       "4          0.553            1.410       0.733            0.678   \n",
       "5          0.487            1.366       0.753            0.613   \n",
       "6          0.533            1.345       0.707            0.638   \n",
       "7          0.534            1.352       0.689            0.663   \n",
       "8          0.406            1.403       0.791            0.612   \n",
       "9          0.419            1.254       0.731            0.523   \n",
       "\n",
       "   RefTverskyCombo  RefTversky  RefColorTversky   Overlap  \n",
       "0            2.000       1.000            1.000  1166.528  \n",
       "1            1.817       1.000            0.817  1166.285  \n",
       "2            1.707       0.891            0.816  1043.744  \n",
       "3            1.515       0.900            0.615  1046.400  \n",
       "4            1.638       0.888            0.750  1047.908  \n",
       "5            1.600       0.898            0.702  1058.909  \n",
       "6            1.630       0.866            0.764  1023.373  \n",
       "7            1.593       0.859            0.734  1016.538  \n",
       "8            1.450       0.903            0.546  1062.149  \n",
       "9            1.647       0.970            0.678  1152.017  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_default = pd.read_csv(\"data/basic_run/roshambo.csv\", delimiter=\"\\t\")\n",
    "df_default.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a4ef77",
   "metadata": {},
   "source": [
    "## Running with a Different Shape Overlap Calculation Method\n",
    "Two methods are supported for calculating the shape overlaps:\n",
    "1. **Analytic** \n",
    "2. **Grid (numerical integration over a quadrature)**\n",
    "\n",
    "The default is the analytic method with second order overlaps.\n",
    "\n",
    "### Modifying Parameters for the Analytic Overlap Calculations\n",
    "\n",
    "In the following code cell, we will modify our previous `get_similarity_scores function` call to account for higher order overlap when calculating the shape overlap volumes for our query and transformed molecules.\n",
    "\n",
    "This is done by specifying the `n` parameter, which controls the order of overlap we want to consider. In this case, we set n=6 for a more nuanced calculation.\n",
    "\n",
    "Additionally, we can adjust the default cutoff radius in the overlap condition \n",
    "$$| R_i - R_j | \\leq \\sigma_i + \\sigma_j + \\varepsilon$$\n",
    "by providing an `eps` value. Here, we use epsilon=0.5. The default `eps` is 0.1. \n",
    "\n",
    "<span style=\"color:red;\">Note that increasing the values of `n` and `eps` will increase the computational time, but will yield more accurate results.</span>\n",
    "\n",
    "You can also specify the parameter `proxy_cutoff` instead of `eps`, and in this case, the overlap calculations will use this condition instead: \n",
    "\n",
    "$$| R_i - R_j | \\leq cutoff$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "81c5bfa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.469620943069458\n",
      "Preparing mols took: 0.8221597671508789\n",
      "Run time: 0.08481175103224814\n",
      "Running paper took: 0.12379884719848633\n",
      "Converting transformation arrays took: 0.002429485321044922\n",
      "Transforming molecules took: 0.06548810005187988\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating shape scores took: 23.693172693252563\n",
      "Calculating color scores took: 0.4646162986755371\n",
      "Creating dataframe took: 0.1764085292816162\n",
      "Writing molecule file took: 0.235443115234375\n"
     ]
    }
   ],
   "source": [
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.sdf\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=0,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    volume_type=\"analytic\",\n",
    "    n=6,\n",
    "    epsilon=0.5,\n",
    "    # proxy_cutoff = 3, \n",
    "    working_dir=\"data/analytic\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "714636c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule</th>\n",
       "      <th>OriginalName</th>\n",
       "      <th>ComboTanimoto</th>\n",
       "      <th>ShapeTanimoto</th>\n",
       "      <th>ColorTanimoto</th>\n",
       "      <th>FitTverskyCombo</th>\n",
       "      <th>FitTversky</th>\n",
       "      <th>FitColorTversky</th>\n",
       "      <th>RefTverskyCombo</th>\n",
       "      <th>RefTversky</th>\n",
       "      <th>RefColorTversky</th>\n",
       "      <th>Overlap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL221029_0</td>\n",
       "      <td>CHEMBL221029</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>348.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL220585_0</td>\n",
       "      <td>CHEMBL220585</td>\n",
       "      <td>1.762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>1.919</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.919</td>\n",
       "      <td>1.817</td>\n",
       "      <td>0.999</td>\n",
       "      <td>0.817</td>\n",
       "      <td>348.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL557844_0</td>\n",
       "      <td>CHEMBL557844</td>\n",
       "      <td>1.379</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.659</td>\n",
       "      <td>1.569</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.774</td>\n",
       "      <td>1.700</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.816</td>\n",
       "      <td>310.003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL221912_0</td>\n",
       "      <td>CHEMBL221912</td>\n",
       "      <td>1.354</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.521</td>\n",
       "      <td>1.708</td>\n",
       "      <td>0.936</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.499</td>\n",
       "      <td>0.884</td>\n",
       "      <td>0.615</td>\n",
       "      <td>307.215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL221037_0</td>\n",
       "      <td>CHEMBL221037</td>\n",
       "      <td>1.188</td>\n",
       "      <td>0.635</td>\n",
       "      <td>0.553</td>\n",
       "      <td>1.389</td>\n",
       "      <td>0.712</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.605</td>\n",
       "      <td>0.855</td>\n",
       "      <td>0.750</td>\n",
       "      <td>301.509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHEMBL375296_0</td>\n",
       "      <td>CHEMBL375296</td>\n",
       "      <td>1.125</td>\n",
       "      <td>0.590</td>\n",
       "      <td>0.534</td>\n",
       "      <td>1.337</td>\n",
       "      <td>0.674</td>\n",
       "      <td>0.663</td>\n",
       "      <td>1.559</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.734</td>\n",
       "      <td>291.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEMBL221376_0</td>\n",
       "      <td>CHEMBL221376</td>\n",
       "      <td>1.122</td>\n",
       "      <td>0.589</td>\n",
       "      <td>0.533</td>\n",
       "      <td>1.304</td>\n",
       "      <td>0.666</td>\n",
       "      <td>0.638</td>\n",
       "      <td>1.600</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.764</td>\n",
       "      <td>295.265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL222027_0</td>\n",
       "      <td>CHEMBL222027</td>\n",
       "      <td>1.116</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.487</td>\n",
       "      <td>1.320</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1.554</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.702</td>\n",
       "      <td>300.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHEMBL1081138_0</td>\n",
       "      <td>CHEMBL1081138</td>\n",
       "      <td>1.066</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.406</td>\n",
       "      <td>1.384</td>\n",
       "      <td>0.772</td>\n",
       "      <td>0.612</td>\n",
       "      <td>1.366</td>\n",
       "      <td>0.819</td>\n",
       "      <td>0.546</td>\n",
       "      <td>286.527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHEMBL1079594_0</td>\n",
       "      <td>CHEMBL1079594</td>\n",
       "      <td>1.048</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.419</td>\n",
       "      <td>1.189</td>\n",
       "      <td>0.667</td>\n",
       "      <td>0.523</td>\n",
       "      <td>1.597</td>\n",
       "      <td>0.919</td>\n",
       "      <td>0.678</td>\n",
       "      <td>327.313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Molecule   OriginalName  ComboTanimoto  ShapeTanimoto  \\\n",
       "0   CHEMBL221029_0   CHEMBL221029          2.000          1.000   \n",
       "1   CHEMBL220585_0   CHEMBL220585          1.762          1.000   \n",
       "2   CHEMBL557844_0   CHEMBL557844          1.379          0.720   \n",
       "3   CHEMBL221912_0   CHEMBL221912          1.354          0.834   \n",
       "4   CHEMBL221037_0   CHEMBL221037          1.188          0.635   \n",
       "5   CHEMBL375296_0   CHEMBL375296          1.125          0.590   \n",
       "6   CHEMBL221376_0   CHEMBL221376          1.122          0.589   \n",
       "7   CHEMBL222027_0   CHEMBL222027          1.116          0.630   \n",
       "8  CHEMBL1081138_0  CHEMBL1081138          1.066          0.660   \n",
       "9  CHEMBL1079594_0  CHEMBL1079594          1.048          0.630   \n",
       "\n",
       "   ColorTanimoto  FitTverskyCombo  FitTversky  FitColorTversky  \\\n",
       "0          1.000            2.000       1.000            1.000   \n",
       "1          0.762            1.919       1.000            0.919   \n",
       "2          0.659            1.569       0.796            0.774   \n",
       "3          0.521            1.708       0.936            0.773   \n",
       "4          0.553            1.389       0.712            0.678   \n",
       "5          0.534            1.337       0.674            0.663   \n",
       "6          0.533            1.304       0.666            0.638   \n",
       "7          0.487            1.320       0.707            0.613   \n",
       "8          0.406            1.384       0.772            0.612   \n",
       "9          0.419            1.189       0.667            0.523   \n",
       "\n",
       "   RefTverskyCombo  RefTversky  RefColorTversky  Overlap  \n",
       "0            2.000       1.000            1.000  348.494  \n",
       "1            1.817       0.999            0.817  348.300  \n",
       "2            1.700       0.884            0.816  310.003  \n",
       "3            1.499       0.884            0.615  307.215  \n",
       "4            1.605       0.855            0.750  301.509  \n",
       "5            1.559       0.825            0.734  291.225  \n",
       "6            1.600       0.835            0.764  295.265  \n",
       "7            1.554       0.852            0.702  300.411  \n",
       "8            1.366       0.819            0.546  286.527  \n",
       "9            1.597       0.919            0.678  327.313  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sixth = pd.read_csv(\"data/analytic/roshambo.csv\", delimiter=\"\\t\")\n",
    "df_sixth.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bda8dc",
   "metadata": {},
   "source": [
    "### Using the Grid Method\n",
    "\n",
    " Add info for grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3082e256",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.4689018726348877\n",
      "Preparing mols took: 0.8317570686340332\n",
      "Run time: 0.08500693505629897\n",
      "Running paper took: 0.11936593055725098\n",
      "Converting transformation arrays took: 0.002772092819213867\n",
      "Transforming molecules took: 0.06621527671813965\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating shape scores took: 1.7001261711120605\n",
      "Calculating color scores took: 0.47480034828186035\n",
      "Creating dataframe took: 0.17479681968688965\n",
      "Writing molecule file took: 0.23421001434326172\n"
     ]
    }
   ],
   "source": [
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.sdf\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=0,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    volume_type=\"gaussian\",\n",
    "    res=0.4,\n",
    "    margin=0.4,\n",
    "    working_dir=\"data/grid\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c42f7a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Molecule</th>\n",
       "      <th>OriginalName</th>\n",
       "      <th>ComboTanimoto</th>\n",
       "      <th>ShapeTanimoto</th>\n",
       "      <th>ColorTanimoto</th>\n",
       "      <th>FitTverskyCombo</th>\n",
       "      <th>FitTversky</th>\n",
       "      <th>FitColorTversky</th>\n",
       "      <th>RefTverskyCombo</th>\n",
       "      <th>RefTversky</th>\n",
       "      <th>RefColorTversky</th>\n",
       "      <th>Overlap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CHEMBL221029_0</td>\n",
       "      <td>CHEMBL221029</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>215.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CHEMBL220585_0</td>\n",
       "      <td>CHEMBL220585</td>\n",
       "      <td>1.762</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.762</td>\n",
       "      <td>1.919</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.919</td>\n",
       "      <td>1.818</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.817</td>\n",
       "      <td>215.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CHEMBL557844_0</td>\n",
       "      <td>CHEMBL557844</td>\n",
       "      <td>1.407</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.659</td>\n",
       "      <td>1.592</td>\n",
       "      <td>0.818</td>\n",
       "      <td>0.774</td>\n",
       "      <td>1.713</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.816</td>\n",
       "      <td>194.160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHEMBL221912_0</td>\n",
       "      <td>CHEMBL221912</td>\n",
       "      <td>1.353</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.521</td>\n",
       "      <td>1.684</td>\n",
       "      <td>0.911</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.521</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.615</td>\n",
       "      <td>194.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CHEMBL221037_0</td>\n",
       "      <td>CHEMBL221037</td>\n",
       "      <td>1.184</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.553</td>\n",
       "      <td>1.374</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0.678</td>\n",
       "      <td>1.622</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.750</td>\n",
       "      <td>190.185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHEMBL222027_0</td>\n",
       "      <td>CHEMBL222027</td>\n",
       "      <td>1.174</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.487</td>\n",
       "      <td>1.366</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.613</td>\n",
       "      <td>1.590</td>\n",
       "      <td>0.888</td>\n",
       "      <td>0.702</td>\n",
       "      <td>192.919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CHEMBL221376_0</td>\n",
       "      <td>CHEMBL221376</td>\n",
       "      <td>1.152</td>\n",
       "      <td>0.619</td>\n",
       "      <td>0.533</td>\n",
       "      <td>1.332</td>\n",
       "      <td>0.694</td>\n",
       "      <td>0.638</td>\n",
       "      <td>1.616</td>\n",
       "      <td>0.852</td>\n",
       "      <td>0.764</td>\n",
       "      <td>185.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CHEMBL375296_0</td>\n",
       "      <td>CHEMBL375296</td>\n",
       "      <td>1.107</td>\n",
       "      <td>0.573</td>\n",
       "      <td>0.534</td>\n",
       "      <td>1.309</td>\n",
       "      <td>0.646</td>\n",
       "      <td>0.663</td>\n",
       "      <td>1.568</td>\n",
       "      <td>0.834</td>\n",
       "      <td>0.734</td>\n",
       "      <td>182.453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CHEMBL1079594_0</td>\n",
       "      <td>CHEMBL1079594</td>\n",
       "      <td>1.105</td>\n",
       "      <td>0.686</td>\n",
       "      <td>0.419</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.523</td>\n",
       "      <td>1.601</td>\n",
       "      <td>0.923</td>\n",
       "      <td>0.678</td>\n",
       "      <td>201.615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHEMBL375205_0</td>\n",
       "      <td>CHEMBL375205</td>\n",
       "      <td>1.094</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.463</td>\n",
       "      <td>1.317</td>\n",
       "      <td>0.695</td>\n",
       "      <td>0.622</td>\n",
       "      <td>1.517</td>\n",
       "      <td>0.873</td>\n",
       "      <td>0.644</td>\n",
       "      <td>190.557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Molecule   OriginalName  ComboTanimoto  ShapeTanimoto  \\\n",
       "0   CHEMBL221029_0   CHEMBL221029          2.000          1.000   \n",
       "1   CHEMBL220585_0   CHEMBL220585          1.762          1.000   \n",
       "2   CHEMBL557844_0   CHEMBL557844          1.407          0.748   \n",
       "3   CHEMBL221912_0   CHEMBL221912          1.353          0.832   \n",
       "4   CHEMBL221037_0   CHEMBL221037          1.184          0.631   \n",
       "5   CHEMBL222027_0   CHEMBL222027          1.174          0.687   \n",
       "6   CHEMBL221376_0   CHEMBL221376          1.152          0.619   \n",
       "7   CHEMBL375296_0   CHEMBL375296          1.107          0.573   \n",
       "8  CHEMBL1079594_0  CHEMBL1079594          1.105          0.686   \n",
       "9   CHEMBL375205_0   CHEMBL375205          1.094          0.631   \n",
       "\n",
       "   ColorTanimoto  FitTverskyCombo  FitTversky  FitColorTversky  \\\n",
       "0          1.000            2.000       1.000            1.000   \n",
       "1          0.762            1.919       1.000            0.919   \n",
       "2          0.659            1.592       0.818            0.774   \n",
       "3          0.521            1.684       0.911            0.773   \n",
       "4          0.553            1.374       0.696            0.678   \n",
       "5          0.487            1.366       0.752            0.613   \n",
       "6          0.533            1.332       0.694            0.638   \n",
       "7          0.534            1.309       0.646            0.663   \n",
       "8          0.419            1.250       0.728            0.523   \n",
       "9          0.463            1.317       0.695            0.622   \n",
       "\n",
       "   RefTverskyCombo  RefTversky  RefColorTversky  Overlap  \n",
       "0            2.000       1.000            1.000  215.170  \n",
       "1            1.818       1.000            0.817  215.206  \n",
       "2            1.713       0.897            0.816  194.160  \n",
       "3            1.521       0.906            0.615  194.859  \n",
       "4            1.622       0.872            0.750  190.185  \n",
       "5            1.590       0.888            0.702  192.919  \n",
       "6            1.616       0.852            0.764  185.660  \n",
       "7            1.568       0.834            0.734  182.453  \n",
       "8            1.601       0.923            0.678  201.615  \n",
       "9            1.517       0.873            0.644  190.557  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grid = pd.read_csv(\"data/grid/roshambo.csv\", delimiter=\"\\t\")\n",
    "df_grid.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc2d24b",
   "metadata": {},
   "source": [
    "### Visualizing the Difference Between Overlap Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30697848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged_df = pd.merge(df_default, df_sixth, on='Molecule', suffixes=('_Default', '_Sixth'))\n",
    "merged_df = pd.merge(merged_df, df_grid, on='Molecule')\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n",
    "axs[0].scatter(merged_df[\"ShapeTanimoto_Default\"], merged_df[\"ShapeTanimoto_Sixth\"], color=\"#80B9F9\")\n",
    "axs[0].set_xlabel(\"ShapeTanimoto (Analytic 2nd)\")\n",
    "axs[0].set_ylabel(\"ShapeTanimoto (Analytic 6th)\")\n",
    "axs[0].set_title(\"Analytic 2nd vs. 6th Order\")\n",
    "axs[0].plot([0, 1], [0, 1], color=\"black\")\n",
    "# Second scatter plot\n",
    "axs[1].scatter(merged_df[\"ShapeTanimoto_Default\"], merged_df[\"ShapeTanimoto\"], color=\"#80B9F9\")\n",
    "axs[1].set_xlabel(\"ShapeTanimoto (Analytic 2nd)\")\n",
    "axs[1].set_ylabel(\"ShapeTanimoto (Grid)\")\n",
    "axs[1].set_title(\"Analytic 2nd vs. Grid\")\n",
    "axs[1].plot([0, 1], [0, 1], color=\"black\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d30148b1",
   "metadata": {},
   "source": [
    "## Running with a Custom Color Force Field \n",
    "\n",
    "In the given function `get_similarity_scores()`, you have the option to specify a different force field definition file using the `fdef_path` parameter. By default, the function uses the BaseFeatures.fdef file from RDKit. However, you can provide a custom JSON file that contains the force field definition.\n",
    "\n",
    "The force field definition file should be in JSON format and should have keys representing the pharmacophore type and corresponding values as a list of SMARTS definitions or patterns. \n",
    "\n",
    "For example, the JSON file could look like this:\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"Donor\": [\"[#16!H0]\"],\n",
    "  \"Acceptor\": [\"[$([O])&!$([OX2](C)C=O)&!$(*(~a)~a)]\"],\n",
    "  \"PosIonizable\": [\"[+,+2,+3,+4]\"],\n",
    "  \"NegIonizable\": [\"[-,-2,-3,-4]\"],\n",
    "  \"Aromatic\": [\"a1aaaaa1\", \"a1aaaa1\"],\n",
    "  \"Hydrophobe\": [\"[C&r3]1~[C&r3]~[C&r3]1\"],   \n",
    "}\n",
    "```\n",
    "\n",
    "Custom JSON files can be found under: roshambo/data directory. For example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65da1fb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.4852163791656494\n",
      "Preparing mols took: 0.8493287563323975\n",
      "Run time: 0.08354299096390605\n",
      "Running paper took: 0.11951994895935059\n",
      "Converting transformation arrays took: 0.002428293228149414\n",
      "Transforming molecules took: 0.0635061264038086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating shape scores took: 0.6596577167510986\n",
      "Calculating color scores took: 0.6335222721099854\n",
      "Creating dataframe took: 0.18086671829223633\n",
      "Writing molecule file took: 0.2169027328491211\n"
     ]
    }
   ],
   "source": [
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.sdf\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=0,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    fdef_path=\"../data/features.json\",\n",
    "    working_dir=\"data/color\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98cd412",
   "metadata": {},
   "source": [
    "### Visualizing the Pharmacophores\n",
    "\n",
    "To visualize the pharmacophores, we will use the `draw_pharm` function from the `pharmacophore` module in ROSHAMBO. In this section, we will explain how to visualize the features on the query molecule. Please note that we will be performing the visualization process from scratch, which involves reading the molecule, identifying the pharmacophores, and more.\n",
    "\n",
    "Alternatively, you can choose to visualize the pharmacophores within the `get_similarity_scores` function while running the actual calculations. To do this, simply set the `draw_pharm` parameter to True. However, in this particular context, we will not visualize all the outputs.\n",
    "\n",
    "<span style=\"color:red;\">It's important to note the following differences between ROSHAMBO and ROCS when it comes to drawing features:</span>\n",
    "\n",
    "1. ROCS highlights the center of the pharmacophore, whereas ROSHAMBO highlights all the atoms representing the pharmacophore.\n",
    "2. In ROCS, the alpha value of the color of the pharmacophores on the dataset molecule changes based on its overlap with the pharmacophores on the query molecule. On the other hand, ROSHAMBO treats each molecule independently and highlights all the features on each molecule separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d03c3063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from roshambo.pharmacophore import draw_pharm, calc_custom_pharm, load_smarts_from_json, calc_rdkit_pharm\n",
    "\n",
    "compiled_smarts = load_smarts_from_json(\"../data/features.json\")\n",
    "rdkit_mol = Chem.MolFromMolFile(\"data/basic_run/query.sdf\")\n",
    "custom_features = calc_custom_pharm(rdkit_mol, compiled_smarts)\n",
    "rdkit_features = calc_rdkit_pharm(rdkit_mol)\n",
    "draw_pharm(rdkit_mol, custom_features, working_dir=\"data/basic_run\")\n",
    "draw_pharm(rdkit_mol, rdkit_features, working_dir=\"data/basic_run\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85d056b",
   "metadata": {},
   "source": [
    "## Running with Conformer Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fac76ed",
   "metadata": {},
   "source": [
    "Now, let's run a similarity calculation that involves conformer generation, energy calculations, and conformer selection. \n",
    "\n",
    "**In the following run, we will do the following**:\n",
    "1. Generate 10 conformers for each molecule using the \"ETKDGv3\" method in RDKit.\n",
    "2. Calculate the energy of the conformers and retain only those with an energy difference less than or equal to 30 kcal/mol from the minimum energy conformer (controlled by the `energy_cutoff` parameter).\n",
    "3. Retain conformers that are at least 0.1 Angstroms apart from each other (controlled by the `rms_cutoff` parameter).\n",
    "\n",
    "You can also choose to optimize the conformers by setting `opt_confs` to True, in which case you can optionally set the `ff` parameter to your force field of choice. `ff` defaults to `MMFF94s`.\n",
    "\n",
    "<span style=\"color:red;\">If `keep_mol` is set to True, the original molecule will be retained in the overlay optimization and score calculations.</span>\n",
    "\n",
    "`max_conformers` specifies how many conformers of each dataset molecule will be saved in the final sdf and csv files. For example `max_conformers` = 1 will return the best conformer (based on the score specified by `sort_by`). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "176b3438",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.6353604793548584\n",
      "Preparing mols took: 6.987128496170044\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run time: 0.8200431291479617\n",
      "Running paper took: 1.0259017944335938\n",
      "Converting transformation arrays took: 0.029053211212158203\n",
      "Transforming molecules took: 0.6692755222320557\n",
      "Calculating shape scores took: 2.9752635955810547\n",
      "Calculating color scores took: 1.923708438873291\n",
      "Creating dataframe took: 0.17370104789733887\n",
      "Writing molecule file took: 0.4825108051300049\n"
     ]
    }
   ],
   "source": [
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.sdf\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=10,\n",
    "    keep_mol=True,\n",
    "    random_seed=109838974,\n",
    "    opt_confs=True,\n",
    "    calc_energy=True,\n",
    "    energy_iters=300,\n",
    "    energy_cutoff=30,\n",
    "    align_confs=True,\n",
    "    rms_cutoff=0.1,\n",
    "    num_threads=48,\n",
    "    method=\"ETKDGv3\",\n",
    "    volume_type=\"analytic\",\n",
    "    n=2,\n",
    "    epsilon=0.5,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    max_conformers=3,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    fdef_path=\"../data/features.json\",\n",
    "    working_dir=\"data/conformers\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06d438ce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-02-08T17:27:49.777301Z",
     "start_time": "2024-02-08T17:27:49.736851Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "           Molecule  OriginalName  ComboTanimoto  ShapeTanimoto   \n0    CHEMBL221029_0  CHEMBL221029          2.000          1.000  \\\n1    CHEMBL220585_0  CHEMBL220585          1.705          1.000   \n2  CHEMBL222027_0_9  CHEMBL222027          1.565          0.771   \n3  CHEMBL375076_0_8  CHEMBL375076          1.485          0.805   \n4  CHEMBL375076_0_9  CHEMBL375076          1.485          0.805   \n5  CHEMBL375076_0_7  CHEMBL375076          1.485          0.805   \n6    CHEMBL557844_0  CHEMBL557844          1.466          0.758   \n7  CHEMBL221029_0_4  CHEMBL221029          1.449          0.768   \n8  CHEMBL221029_0_5  CHEMBL221029          1.422          0.767   \n9  CHEMBL557844_0_9  CHEMBL557844          1.420          0.735   \n\n   ColorTanimoto  FitTverskyCombo  FitTversky  FitColorTversky   \n0          1.000            2.000       1.000            1.000  \\\n1          0.705            1.883       1.000            0.883   \n2          0.794            1.646       0.803            0.843   \n3          0.680            1.706       0.896            0.810   \n4          0.680            1.706       0.896            0.810   \n5          0.680            1.706       0.896            0.810   \n6          0.709            1.624       0.835            0.789   \n7          0.682            1.684       0.873            0.811   \n8          0.654            1.664       0.873            0.791   \n9          0.685            1.596       0.822            0.774   \n\n   RefTverskyCombo  RefTversky  RefColorTversky   Overlap  \n0            2.000       1.000            1.000  1169.784  \n1            1.778       1.000            0.778  1169.540  \n2            1.883       0.951            0.932  1123.398  \n3            1.697       0.887            0.809  1037.446  \n4            1.697       0.887            0.809  1037.446  \n5            1.697       0.887            0.809  1037.445  \n6            1.765       0.891            0.874  1046.460  \n7            1.675       0.864            0.810  1010.495  \n8            1.655       0.864            0.791  1010.137  \n9            1.730       0.874            0.856  1025.487  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Molecule</th>\n      <th>OriginalName</th>\n      <th>ComboTanimoto</th>\n      <th>ShapeTanimoto</th>\n      <th>ColorTanimoto</th>\n      <th>FitTverskyCombo</th>\n      <th>FitTversky</th>\n      <th>FitColorTversky</th>\n      <th>RefTverskyCombo</th>\n      <th>RefTversky</th>\n      <th>RefColorTversky</th>\n      <th>Overlap</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>CHEMBL221029_0</td>\n      <td>CHEMBL221029</td>\n      <td>2.000</td>\n      <td>1.000</td>\n      <td>1.000</td>\n      <td>2.000</td>\n      <td>1.000</td>\n      <td>1.000</td>\n      <td>2.000</td>\n      <td>1.000</td>\n      <td>1.000</td>\n      <td>1169.784</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CHEMBL220585_0</td>\n      <td>CHEMBL220585</td>\n      <td>1.705</td>\n      <td>1.000</td>\n      <td>0.705</td>\n      <td>1.883</td>\n      <td>1.000</td>\n      <td>0.883</td>\n      <td>1.778</td>\n      <td>1.000</td>\n      <td>0.778</td>\n      <td>1169.540</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CHEMBL222027_0_9</td>\n      <td>CHEMBL222027</td>\n      <td>1.565</td>\n      <td>0.771</td>\n      <td>0.794</td>\n      <td>1.646</td>\n      <td>0.803</td>\n      <td>0.843</td>\n      <td>1.883</td>\n      <td>0.951</td>\n      <td>0.932</td>\n      <td>1123.398</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>CHEMBL375076_0_8</td>\n      <td>CHEMBL375076</td>\n      <td>1.485</td>\n      <td>0.805</td>\n      <td>0.680</td>\n      <td>1.706</td>\n      <td>0.896</td>\n      <td>0.810</td>\n      <td>1.697</td>\n      <td>0.887</td>\n      <td>0.809</td>\n      <td>1037.446</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>CHEMBL375076_0_9</td>\n      <td>CHEMBL375076</td>\n      <td>1.485</td>\n      <td>0.805</td>\n      <td>0.680</td>\n      <td>1.706</td>\n      <td>0.896</td>\n      <td>0.810</td>\n      <td>1.697</td>\n      <td>0.887</td>\n      <td>0.809</td>\n      <td>1037.446</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>CHEMBL375076_0_7</td>\n      <td>CHEMBL375076</td>\n      <td>1.485</td>\n      <td>0.805</td>\n      <td>0.680</td>\n      <td>1.706</td>\n      <td>0.896</td>\n      <td>0.810</td>\n      <td>1.697</td>\n      <td>0.887</td>\n      <td>0.809</td>\n      <td>1037.445</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CHEMBL557844_0</td>\n      <td>CHEMBL557844</td>\n      <td>1.466</td>\n      <td>0.758</td>\n      <td>0.709</td>\n      <td>1.624</td>\n      <td>0.835</td>\n      <td>0.789</td>\n      <td>1.765</td>\n      <td>0.891</td>\n      <td>0.874</td>\n      <td>1046.460</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>CHEMBL221029_0_4</td>\n      <td>CHEMBL221029</td>\n      <td>1.449</td>\n      <td>0.768</td>\n      <td>0.682</td>\n      <td>1.684</td>\n      <td>0.873</td>\n      <td>0.811</td>\n      <td>1.675</td>\n      <td>0.864</td>\n      <td>0.810</td>\n      <td>1010.495</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>CHEMBL221029_0_5</td>\n      <td>CHEMBL221029</td>\n      <td>1.422</td>\n      <td>0.767</td>\n      <td>0.654</td>\n      <td>1.664</td>\n      <td>0.873</td>\n      <td>0.791</td>\n      <td>1.655</td>\n      <td>0.864</td>\n      <td>0.791</td>\n      <td>1010.137</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CHEMBL557844_0_9</td>\n      <td>CHEMBL557844</td>\n      <td>1.420</td>\n      <td>0.735</td>\n      <td>0.685</td>\n      <td>1.596</td>\n      <td>0.822</td>\n      <td>0.774</td>\n      <td>1.730</td>\n      <td>0.874</td>\n      <td>0.856</td>\n      <td>1025.487</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_conformers = pd.read_csv(\"data/conformers/roshambo.csv\", delimiter=\"\\t\")\n",
    "df_conformers.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ced07a5",
   "metadata": {},
   "source": [
    "## Running with SMILES input\n",
    "\n",
    "When using the `get_similarity_scores` function with .smi input files, the calculation process remains the same as before. However, you can optionally include an additional parameter called `smiles_kwargs` to customize the parameters passed to the `rdkit.Chem.rdmolfiles.SmilesMolSupplier` function when reading molecules from the .smi file.\n",
    "\n",
    "<span style=\"color:red;\">Note that sometimes you need to provide the delimiter used to separate the SMILES from the molecule name on each line of the file. The default used in RDKit is space. </span>\n",
    "\n",
    "Like before, the `dataset_files_pattern` parameter allows you to specify a file pattern corresponding to multiple files. This enables reading and processing multiple dataset files together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "018547a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Preparing mols took: 0.7570054531097412\n",
      "Preparing mols took: 15.808820962905884\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "# Executing PAPER on GPU 0\n",
      "# Shape overlay optimization used 10 iterations of BFGS\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run time: 0.47548187407664955\n",
      "Running paper took: 1.185603380203247\n",
      "Converting transformation arrays took: 0.18064665794372559\n",
      "Transforming molecules took: 3.3388750553131104\n",
      "Calculating shape scores took: 10.02515459060669\n",
      "Calculating color scores took: 6.905524253845215\n",
      "Creating dataframe took: 0.17230749130249023\n",
      "Writing molecule file took: 0.21876978874206543\n"
     ]
    }
   ],
   "source": [
    "get_similarity_scores(\n",
    "    ref_file=\"query.sdf\",\n",
    "    dataset_files_pattern=\"dataset.smi\",\n",
    "    ignore_hs=True,\n",
    "    n_confs=100,\n",
    "    keep_mol=True,\n",
    "    random_seed=109838974,\n",
    "    opt_confs=False,\n",
    "    calc_energy=False,\n",
    "    energy_iters=300,\n",
    "    energy_cutoff=30,\n",
    "    align_confs=True,\n",
    "    rms_cutoff=0.1,\n",
    "    num_threads=48,\n",
    "    method=\"ETKDGv3\",\n",
    "    volume_type=\"analytic\",\n",
    "    n=2,\n",
    "    epsilon=0.5,\n",
    "    use_carbon_radii=True,\n",
    "    color=True,\n",
    "    max_conformers=1,\n",
    "    sort_by=\"ComboTanimoto\",\n",
    "    write_to_file=True,\n",
    "    gpu_id=0,\n",
    "    fdef_path=\"../data/features.json\",\n",
    "    working_dir=\"data/smiles\",\n",
    "    #smiles_kwargs={\"delimiter\": \"\\t\"},\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13707cb",
   "metadata": {},
   "source": [
    "## ROC Analysis \n",
    "\n",
    "\n",
    "We will now explore the analysis capabilities provided by ROSHAMBO. The analysis includes:\n",
    "1. Calculating the Receiver Operating Characteristic (ROC) curve\n",
    "2. Determining the Area Under the Curve (AUC) value\n",
    "3. Calculating the Enrichment Factor at different rates\n",
    "\n",
    "These calculations are performed using bootstrapping techniques to obtain statistical measures such as mean and 95% confidence intervals. \n",
    "\n",
    "ROSHAMBO also allows plotting the following:\n",
    "1. Distribution of scores from a dataset (e.g. obtained from ROSHAMBO or other methods)\n",
    "2. ROC curves in both normal and semi-log formats, allowing for easy comparison between different outputs such as ROCS and ROSHAMBO\n",
    "3. AUC bar plots, grouping datasets by method (e.g. to compare between ROSHAMBO and other methods) \n",
    "4. Stacked enrichment bar plots can be, again grouping datasets by method \n",
    "\n",
    "This kind of analysis/visualization can be performed via the `analysis` module. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11973de6",
   "metadata": {},
   "source": [
    "### Score Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a5ef8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1800 with 3 Axes>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from roshambo.analysis import plot_scores_dist\n",
    "\n",
    "working_dir = \"data/analysis\"\n",
    "df = pd.read_csv(f\"{working_dir}/roshambo_ligands_CSF1R.csv\", delimiter=\"\\t\")\n",
    "plot_scores_dist(\n",
    "    df,\n",
    "    columns=[\"ComboTanimoto\", \"ShapeTanimoto\", \"ColorTanimoto\"],\n",
    "    title=\"CSF1R Score Distributions\",\n",
    "    working_dir=working_dir,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e22e3d",
   "metadata": {},
   "source": [
    "### ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5fed898e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from roshambo.analysis import calc_roc_auc\n",
    "\n",
    "auc, roce, fig = calc_roc_auc(\n",
    "    f\"{working_dir}/roshambo_ligands_CXCR4.csv\",\n",
    "    f\"{working_dir}/roshambo_decoys_CXCR4.csv\",\n",
    "    score=\"ComboTanimoto\",\n",
    "    n_bootstraps=1000,\n",
    "    interpolation=True,\n",
    "    eevs=[0.005, 0.01, 0.02, 0.05],\n",
    "    plot=True,\n",
    "    log=False,\n",
    "    working_dir=working_dir,\n",
    ")\n",
    "shutil.move(f\"{working_dir}/roc.csv\", f\"{working_dir}/roshambo_roc_CXCR4.csv\")\n",
    "shutil.move(f\"{working_dir}/analysis.csv\", f\"{working_dir}/roshambo_analysis_CXCR4.csv\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a92d9f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "auc, roce, fig = calc_roc_auc(\n",
    "    f\"{working_dir}/roshambo_ligands_CSF1R.csv\",\n",
    "    f\"{working_dir}/roshambo_decoys_CSF1R.csv\",\n",
    "    score=\"ComboTanimoto\",\n",
    "    n_bootstraps=1000,\n",
    "    interpolation=True,\n",
    "    eevs=[0.005, 0.01, 0.02, 0.05],\n",
    "    plot=True,\n",
    "    log=False,\n",
    "    working_dir=working_dir,\n",
    ")\n",
    "\n",
    "shutil.move(f\"{working_dir}/roc.csv\", f\"{working_dir}/roshambo_roc_CSF1R.csv\")\n",
    "shutil.move(f\"{working_dir}/analysis.csv\", f\"{working_dir}/roshambo_analysis_CSF1R.csv\")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1e4edce",
   "metadata": {},
   "source": [
    "### Multiple ROC Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc2d4d02",
   "metadata": {
    "is_executing": true
   },
   "outputs": [],
   "source": [
    "from roshambo.analysis import plot_mult_roc\n",
    "\n",
    "fig_cxcr4_csf1r = plot_mult_roc(\n",
    "    rates_dict={\n",
    "        \"CXCR4\": f\"{working_dir}/roshambo_roc_CXCR4.csv\",\n",
    "        \"CSF1R\": f\"{working_dir}/roshambo_roc_CSF1R.csv\"\n",
    "    },\n",
    "    analysis_dict={\n",
    "        \"CXCR4\": f\"{working_dir}/roshambo_analysis_CXCR4.csv\", \"CSF1R\": f\"{working_dir}/roshambo_analysis_CSF1R.csv\"},\n",
    "    colors_dict={\"CXCR4\": \"#80B9F9\", \"CSF1R\": \"#6DAD46\"},\n",
    "    title=\"CXCR4 vs. CSF1R\",\n",
    "    log=False,\n",
    "    filename=\"CXCR4_CSF1R.jpg\",\n",
    "    working_dir=working_dir,\n",
    ")\n",
    "\n",
    "fig_cxcr4_csf1r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e45f61d",
   "metadata": {},
   "source": [
    "### AUC Plot\n",
    "The following function `plot_mult_auc` allows you to plot the AUC values obtained from the ROC analysis from different software for comparison. Here, only ROSHAMBO is used for demonstration purposes but you can include other software as well as shown in the commented part of the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05d43b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from roshambo.analysis import plot_mult_auc\n",
    "\n",
    "fig = plot_mult_auc(\n",
    "    auc_dict={\n",
    "        \"ROSHAMBO\": [\n",
    "            f\"{working_dir}/roshambo_analysis_CSF1R.csv\",\n",
    "            f\"{working_dir}/roshambo_analysis_CXCR4.csv\",\n",
    "        ],\n",
    "        # \"Other_Software\": [\n",
    "        #     f\"{working_dir}/software_analysis_CSF1R.csv\",\n",
    "        #     f\"{working_dir}/software_analysis_CXCR4.csv\",\n",
    "        # ],\n",
    "    },\n",
    "    colors_dict={\"ROSHAMBO\": \"#80B9F9\"},\n",
    "    # colors_dict={\"ROSHAMBO\": \"#80B9F9\", \"Software\": \"#6DAD46\"},\n",
    "    group_labels=[\"CSF1R\", \"CXCR4\"],\n",
    "    working_dir=working_dir,\n",
    ")\n",
    "\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106906e8",
   "metadata": {},
   "source": [
    "### Enrichment Factors Plot\n",
    "The following function `plot_mult_enrichment` allows you to plot the enrichment factors obtained from different software for comparison. Here, only ROSHAMBO is used for demonstration purposes but you can include other software as well as shown in the commented part of the code. The bars will be grouped by the software used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fda1fcf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from roshambo.analysis import plot_mult_enrichment\n",
    "\n",
    "fig_enrich = plot_mult_enrichment(\n",
    "    enrich_dict={\n",
    "        \"ROSHAMBO\": [\n",
    "            f\"{working_dir}/roshambo_analysis_CSF1R.csv\",\n",
    "            f\"{working_dir}/roshambo_analysis_CXCR4.csv\",\n",
    "        ],\n",
    "        # \"Other_Software\": [\n",
    "        #     f\"{working_dir}/software_analysis_CSF1R.csv\",\n",
    "        #     f\"{working_dir}/software_analysis_CXCR4.csv\",\n",
    "        # ],\n",
    "    },\n",
    "    colors_dict={0: \"#80B9F9\", 1: \"#6DAD46\", 2: \"gray\", 3: \"black\"},\n",
    "    hatch_patterns=[None, \"+\"],\n",
    "    group_labels=[\"CSF1R\", \"CXCR4\"],\n",
    "    working_dir=working_dir,\n",
    ")\n",
    "\n",
    "fig_enrich"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
